Search Results - "Chada, Neil K"

Cauchy Markov random field priors for Bayesian inversion by Suuronen, Jarkko, Chada, Neil K., Roininen, Lassi

“…The use of Cauchy Markov random field priors in statistical inverse problems can potentially lead to posterior distributions which are non-Gaussian,…”
Get full text

Multilevel estimation of normalization constants using ensemble Kalman–Bucy filters by Ruzayqat, Hamza, Chada, Neil K., Jasra, Ajay

“…In this article we consider the application of multilevel Monte Carlo, for the estimation of normalizing constants. In particular we will make use of the…”
Get full text

A Review of the EnKF for Parameter Estimation by Chada, Neil K

“…The ensemble Kalman filter is a well-known and celebrated data assimilation algorithm. It is of particular relevance as it used for high-dimensional problems,…”
Get full text

Unbiased Approximations for Stationary Distributions of McKean-Vlasov SDEs by Awadelkarim, Elsiddig, Chada, Neil K, Jasra, Ajay

“…We consider the development of unbiased estimators, to approximate the stationary distribution of Mckean-Vlasov stochastic differential equations (MVSDEs)…”
Get full text

Learning dynamical systems from data: Gradient-based dictionary optimization by Tabish, Mohammad, Chada, Neil K, Klus, Stefan

“…The Koopman operator plays a crucial role in analyzing the global behavior of dynamical systems. Existing data-driven methods for approximating the Koopman…”
Get full text

A Statistical Framework and Analysis for Perfect Radar Pulse Compression by Chada, Neil K, Piiroinen, Petteri, Roininen, Lassi

“…Perfect radar pulse compression coding is a potential emerging field which aims at providing rigorous analysis and fundamental limit radar experiments. It is…”
Get full text

The Stochastic Steepest Descent Method for Robust Optimization in Banach Spaces by Chada, Neil K, Herbert, Philip J

“…Stochastic gradient methods have been a popular and powerful choice of optimization methods, aimed at minimizing functions. Their advantage lies in the fact…”
Get full text

Sampling from Bayesian Neural Network Posteriors with Symmetric Minibatch Splitting Langevin Dynamics by Paulin, Daniel, Whalley, Peter A, Chada, Neil K, Leimkuhler, Benedict

“…We propose a scalable kinetic Langevin dynamics algorithm for sampling parameter spaces of big data and AI applications. Our scheme combines a symmetric…”
Get full text

A Stochastic Iteratively Regularized Gauss-Newton Method by Bergou, El Houcine, Chada, Neil K, Diouane, Youssef

“…This work focuses on developing and motivating a stochastic version of a wellknown inverse problem methodology. Specifically, we consider the iteratively…”
Get full text

Unbiased Estimation of the Vanilla and Deterministic Ensemble Kalman-Bucy Filters by Alvarez, Miguel, Chada, Neil K, Jasra, Ajay

“…In this article we consider the development of an unbiased estimator for the ensemble Kalman--Bucy filter (EnKBF). The EnKBF is a continuous-time filtering…”
Get full text

Unbiased Estimation using Underdamped Langevin Dynamics by Ruzayqat, Hamza, Chada, Neil K, Jasra, Ajay

“…In this work we consider the unbiased estimation of expectations w.r.t.~probability measures that have non-negative Lebesgue density, and which are known…”
Get full text

The Ensemble Kalman Filter for Dynamic Inverse Problems by Weissmann, Simon, Chada, Neil K, Tong, Xin T

“…In inverse problems, the goal is to estimate unknown model parameters from noisy observational data. Traditionally, inverse problems are solved under the…”
Get full text

Unbiased Kinetic Langevin Monte Carlo with Inexact Gradients by Chada, Neil K, Leimkuhler, Benedict, Paulin, Daniel, Whalley, Peter A

“…We present an unbiased method for Bayesian posterior means based on kinetic Langevin dynamics that combines advanced splitting methods with enhanced gradient…”
Get full text

Unbiased Estimation of the Hessian for Partially Observed Diffusions by Chada, Neil K, Jasra, Ajay, Yu, Fangyuan

“…In this article we consider the development of unbiased estimators of the Hessian, of the log-likelihood function with respect to parameters, for partially…”
Get full text

Multilevel Estimation of Normalization Constants Using the Ensemble Kalman-Bucy Filter by Ruzayqat, Hamza, Chada, Neil K, Jasra, Ajay

“…In this article we consider the application of multilevel Monte Carlo, for the estimation of normalizing constants. In particular we will make use of the…”
Get full text

Cauchy Markov Random Field Priors for Bayesian Inversion by Chada, Neil K, Roininen, Lassi, Suuronen, Jarkko

“…The use of Cauchy Markov random field priors in statistical inverse problems can potentially lead to posterior distributions which are non-Gaussian,…”
Get full text

A Data-Adaptive Prior for Bayesian Learning of Kernels in Operators by Chada, Neil K, Lang, Quanjun, Lu, Fei, Wang, Xiong

“…Kernels are efficient in representing nonlocal dependence and they are widely used to design operators between function spaces. Thus, learning kernels in…”
Get full text

Bayesian inversion with {\alpha}-stable priors by Suuronen, Jarkko, Soto, Tomás, Chada, Neil K, Roininen, Lassi

“…We propose to use L\'evy {\alpha}-stable distributions for constructing priors for Bayesian inverse problems. The construction is based on Markov fields with…”
Get full text

Analysis of Hierarchical Ensemble Kalman Inversion by Chada, Neil K

“…We discuss properties of hierarchical Bayesian inversion through the ensemble Kalman filter (EnKF). Our focus will be primarily on deriving continuous-time…”
Get full text

On a Dynamic Variant of the Iteratively Regularized Gauss-Newton Method with Sequential Data by Chada, Neil K, Iglesias, Marco A, Lu, Shuai, Werner, Frank

“…For numerous parameter and state estimation problems, assimilating new data as they become available can help produce accurate and fast inference of unknown…”
Get full text